{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T04:00:46Z","timestamp":1785297646266,"version":"3.55.0"},"reference-count":45,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Asia-Pac J Clncl Oncology"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>Tumor heterogeneity, driven by metabolic reprogramming, challenges colorectal cancer (CRC) treatment. Methionine metabolism is crucial for tumor progression, but its role in CRC heterogeneity and the tumor immune microenvironment (TIME) requires systematic investigation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A systematic evaluation of 101 combinations of machine learning and statistical algorithms was conducted within a 10\u2010fold cross\u2010validation framework to develop and validate the optimal model, termed the methionine metabolism\u2010related risk score (MMRS). The Cancer Genome Atlas\u2010Colon Adenocarcinoma (TCGA\u2010COAD) dataset served as the training cohort, while two independent Gene Expression Omnibus (GEO) cohorts (GSE39582 and GSE17536) were employed for external validation. Immune infiltration was assessed using the microenvironment cell populations\u2010counter (MCP\u2010counter) algorithm. Model discrimination was evaluated using time\u2010dependent receiver operating characteristic (ROC) analysis, with the area under the curve (AUC) calculated at 1, 3, and 5 years across all cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Methionine metabolism\u2010high (MMH) and metabolism\u2010low (MML) subtypes were defined via unsupervised clustering. The MMH subtype exhibited significantly poorer overall survival and an immunosuppressive TIME. From subtype\u2010associated differentially expressed genes (DEGs), 41 prognostic genes were identified. The optimal model (StepCox[both] + plsRcox) formed the MMRS, which effectively stratified patients into high\u2010 and low\u2010risk groups with significantly different survival across all cohorts (all\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.05). Core signature genes (\n                      <jats:italic>MID2, KIF7, GSR<\/jats:italic>\n                      ) were consistently selected. The high\u2010risk group showed depleted antitumor immunity (e.g., fewer CD8+ T and NK cells) and a stroma\u2010rich phenotype with enrichment of cancer\u2010associated fibroblasts, underpinned by oxidative stress and alterations in energy metabolism pathways. Time\u2010dependent ROC analysis confirmed the discriminative capacity of the MMRS, with 5\u2010year AUCs of 0.803 (TCGA\u2010COAD), 0.632 (GSE39582), and 0.608 (GSE17536), respectively, further supporting its prognostic accuracy across independent patient populations.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Methionine metabolism heterogeneity is a key determinant of prognosis and immune contexture in CRC. The MMRS is a prognostic signature that effectively stratifies CRC patients by risk, though its clinical utility as a predictive biomarker for treatment selection requires prospective validation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ajco.70147","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T03:11:20Z","timestamp":1785294680000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identification of Methionine Metabolism\u2010Driven Heterogeneous Subtypes in Colorectal Cancer and Their Associated Immune Microenvironment"],"prefix":"10.1111","author":[{"given":"Junhao","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Gastrointestinal Surgery The Eighth Affiliated Hospital Southern Medical University (The First People's Hospital of Shunde, Foshan), Shunde  Foshan China"},{"name":"The Second School of Clinical Medicine Southern Medical University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanhua","family":"Deng","sequence":"additional","affiliation":[{"name":"Department of Gastrointestinal Surgery The Eighth Affiliated Hospital Southern Medical University (The First People's Hospital of Shunde, Foshan), Shunde  Foshan China"},{"name":"The Second School of Clinical Medicine Southern Medical University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manzhao","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Department of Gastrointestinal Surgery The Eighth Affiliated Hospital Southern Medical University (The First People's Hospital of Shunde, Foshan), Shunde  Foshan China"},{"name":"The Second School of Clinical Medicine Southern Medical University  Guangzhou China"},{"name":"Jianhe People's Hospital Qiandongnan Miao and Dong Autonomous Prefecture  Kaili City China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/immuno5030031"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/08820139.2023.2264906"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21871"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jare.2025.06.089"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms21155311"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2025.1616414"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2025.1532924"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41568\u2010019\u20100187\u20108"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.3390\/ph18050640"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/nyas.12956"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586\u2010020\u20102682\u20101"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2025.1701505"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41568\u2010020\u20100285\u20107"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/s0140\u20106736(19)32319\u20100"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaw5473"},{"key":"e_1_2_12_17_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1023949509487"},{"key":"e_1_2_12_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2014.11.005"},{"key":"e_1_2_12_19_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13059\u2010016\u20101070\u20105"},{"key":"e_1_2_12_20_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature11252"},{"key":"e_1_2_12_21_1","doi-asserted-by":"crossref","unstructured":"H.Wickham R.Fran\u00e7ois L.Henry et\u00a0al. \u201cDplyr: A Grammar of Data Manipulation \u201d (2023) accessed 2025\u201010\u201015 https:\/\/doi.org\/10.32614\/CRAN.package.dplyr.","DOI":"10.32614\/CRAN.package.dplyr"},{"key":"e_1_2_12_22_1","doi-asserted-by":"publisher","DOI":"10.21105\/joss.01686"},{"key":"e_1_2_12_23_1","doi-asserted-by":"crossref","unstructured":"D.Robinson A.Hayes andS.Couch \u201cBroom: Convert Statistical Objects Into Tidy Tibbles \u201d (2025) accessed 2025\u201010\u201015 https:\/\/doi.org\/10.32614\/CRAN.package.broom.","DOI":"10.32614\/CRAN.package.broom"},{"key":"e_1_2_12_24_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btq170"},{"key":"e_1_2_12_25_1","unstructured":"T. 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